Artificial intelligence in education: using state space search and heuristics in mathematics instruction
✍ Scribed by Anthony E. Kelly; D.H. Sleeman; Kenneth J. Gilhooly
- Publisher
- Elsevier Science
- Year
- 1993
- Weight
- 765 KB
- Volume
- 38
- Category
- Article
- ISSN
- 0020-7373
No coin nor oath required. For personal study only.
✦ Synopsis
Two powerful techniques from AI-the use of heuristics, and state space analysis-are currently influencing the practice of pedagogy, particularly in mathematics. The current study compared the performance of college students on a mathematics task as a function of their exposure to each of these techniques. Specifically, it tested the hypotheses that instruction in the use of AI-type cued heuristics, and state space analysis (search) would improve students' solving of trigonometric identities (the target task), and their solving of group-theory identities (the transfer task). Four conditions were developed in which the effects of cued heuristics alone, search alone, search and cued heuristics, and neither search nor cued heuristics were tested. A significant main effect of cued heuristics was found, but the positive effects of using cued heuristics did not transfer to problems involving group theory identities. Little gain was found for those using state space analysis. Implications for the use of these techniques in mathematics instruction are discussed.